final_project

import and prep data

data viz 1

#   Visualize the relationship between trust in government and likelihood of compliance with government policies, introducing country as a third variable. I think this would work well as a line plot or scatterplot, with different countries in different colors.
viz_1 <- covid_data %>% 
ggplot(aes(trust, gov_comp)) +
  geom_smooth(aes(colour = 
                    fct_reorder(country_of_residence, gov_comp)),
             size = 0.5) +
  facet_wrap(~fct_reorder(country_of_residence, gov_comp)) +
  theme_minimal() +
  scale_color_viridis_d() +
  xlab("Trust in government") +
  ylab("Compliance with government policies") +
  labs(title = "Greater trust in government differentially predicts higher compliance",
       color = 'Country of Residence')
viz_1

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

  ## CHRIS' COMMENT:
  ## Really interesting plot, especially with how real this is right now!
  ## Suggestions: These are really minimal, so I just added the two changes I saw:
  ## (1) I reordered the facet_wrapping, so countries are plotted by their average 
  ## gov_comp. (2) I also changed your color label so it wasn't the variable name

  ## I love the spread in governments you have here; so often it's US-centric
  ## I learned from you that you can change the size of the geom_smooth()
  ## directly in the geom_smooth() call; I had a really odd way of doing it that
  ## is way more work. I'll definitely go this route

data viz 2

# Visualize the distribution of adoption of preventive behaviors by country. I think this would work well as a ridgeline plot or boxplots. 
viridis(n = 9)

## [1] "#440154FF" "#472D7BFF" "#3B528BFF" "#2C728EFF" "#21908CFF" "#27AD81FF"
## [7] "#5DC863FF" "#AADC32FF" "#FDE725FF"

viz_2 <- covid_data %>% 
  mutate(country_of_residence = 
           fct_relevel(country_of_residence,
                       "Australia", 
                       "USA", 
                       "Argentina", 
                       "Saudi Arabia", 
                       "Mexico", 
                       "Egypt", 
                       "China", 
                       "Nigeria", 
                       "India")) %>%
  ggplot(aes(prevent, fct_reorder(country_of_residence, prevent))) +
  ggridges::geom_density_ridges(aes(fill = country_of_residence),
                                bandwidth = 0.6,
                                alpha = 0.5,
                                scale = 1.5) +
  scale_fill_manual(values = 
                      c("#440154FF", 
                        "#472D7BFF", 
                        "#3B528BFF", 
                        "#2C728EFF", 
                        "#21908CFF", 
                        "#27AD81FF", 
                        "#5DC863FF", 
                        "#AADC32FF", 
                        "#FDE725FF")) +
  theme_minimal() +
  theme(legend.position = "none") +
  xlab("Number of preventive behaviors adopted") +
  ylab("") +
  labs(title = "Adoption of preventive behaviors by country")
viz_2

  ## CHRIS' COMMENTS: 
  ## Very cool plot, I like the geom_density_ridges() look a lot. 
  ## Great use of semi-transparent plots so we don't miss the other values.
  ## Suggestions: 
  ## Again, these were super small so I just made them. 
  ## (1) My first change is really not to the plot's aesthetic, per se, but 
  ## Daniel had to tell me about not going over 80 characters/line 
  ## a few times (and I kept doing it, haha). So I just put several 
  ## things on new lines. (2) I did another fct_reorder(), 

data viz 3

# Visualize the overlap between each of the predictor variables (trust in gov/perception of gov preparedness/perception of gov performance/risk perception/perceived controllability). I think this would work well as a heatmap or correlogram.
data_for_cor <- covid_data %>% 
  dplyr::select(c(trust, gov_perf, gov_prep, risk, control))
cor <- as.matrix(cor(data_for_cor))

viz_3 <- heatmaply(cor, 
        colors = viridis(n = 256, alpha = 1, begin = 1, end = 0),
        dendrogram = "none",
        grid_color = "white",
        margins = c(60,100,50,20),
        fontsize_col = 8,
        fontsize_row = 8, 
        labCol = colnames(cor),
        labRow = rownames(cor),
        xlab = "", 
        ylab = "",
        main = "Correlational heatmap of predictor variables",
        heatmap_layers = theme(axis.line=element_blank()))
viz_3

# I could do this on an item level...
data_for_cor_full <- covid_data %>%
  select(contains("perceived_control"),
         contains("risk_perception"),
         contains("trust_in_gov"),
         contains("gov_preparedness"),
         contains("gov_performance"))
cor_full <- as.matrix(cor(data_for_cor_full))

viz_3_full <- heatmaply(cor_full, 
        colors = viridis(n = 256, alpha = 1, begin = 1, end = 0),
        dendrogram = "none",
        grid_color = "white",
        margins = c(60,100,50,20),
        fontsize_col = 8,
        fontsize_row = 8, 
        labCol = colnames(cor_full),
        labRow = rownames(cor_full),
        xlab = "", 
        ylab = "",
        main = "Correlational heatmap of predictor variables",
        heatmap_layers = theme(axis.line=element_blank()))
viz_3_full

  ## CHRIS' COMMENTS: 
  ## I had no idea you could do heatmaps this way, I really like how simple
  ## your code makes it, especially when you have so many variables. Good use
  ## of diagonal text on the x-axis. 
  ## I'm not sure if I like the idea of all items as much as the simplified, 
  ## diagram. But it depends if you're doing general communication or a
  ## research based audience. The former would benefit from the simple plot, maybe?
  ## Suggestions:
  ## (1) I couldn't immediately figure out how to add a label to the fill, but 
  ## I would try that. I think even though you have a title, it is still 
  ## helpful to make sure people know. 
  ## (2) I would consider switching the direction of the fill, such that
  ## bright yellow was 1.00 and dark purple was 0.00. I say this just because
  ## of how I perceive highs and lows (like brightness/presence of light), but
  ## that could be just me.